




'''simple docstring'''







from __future__ import annotations

import random
import unittest

from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
    import tensorflow as tf

    from transformers import (
        TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFTransfoXLForSequenceClassification,
        TFTransfoXLLMHeadModel,
        TFTransfoXLModel,
    )

class 	_snake_case:






 def __init__(self       :  str    ,		a       :  List[str]    ,		)       ->   Optional[int]:






    """simple docstring"""


    A__       =     parent
    A__       =     13
    A__       =     7
    A__       =     30
    A__       =     self.seq_length + self.mem_len
    A__       =     15
    A__       =     True
    A__       =     True
    A__       =     99
    A__       =     [10, 50, 80]
    A__       =     32
    A__       =     32
    A__       =     4
    A__       =     8
    A__       =     1_28
    A__       =     2
    A__       =     2
    A__       =     None
    A__       =     1
    A__       =     0
    A__       =     3
    A__       =     self.vocab_size - 1
    A__       =     0.01






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Tuple:






    """simple docstring"""


    A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size    )
    A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size    )

    A__       =     None
    if self.use_labels:
       A__       =     ids_tensor([self.batch_size, self.seq_length]    ,		self.vocab_size    )

    A__       =     TransfoXLConfig(
        vocab_size=self.vocab_size    ,		mem_len=self.mem_len    ,		clamp_len=self.clamp_len    ,		cutoffs=self.cutoffs    ,		d_model=self.hidden_size    ,		d_embed=self.d_embed    ,		n_head=self.num_attention_heads    ,		d_head=self.d_head    ,		d_inner=self.d_inner    ,		div_val=self.div_val    ,		n_layer=self.num_hidden_layers    ,		eos_token_id=self.eos_token_id    ,		pad_token_id=self.vocab_size - 1    ,		init_range=self.init_range    ,		num_labels=self.num_labels    ,		)

    return (config, input_ids_a, input_ids_a, lm_labels)






 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[int]:






    """simple docstring"""


    random.seed(self.seed    )
    tf.random.set_seed(self.seed    )






 def 						_UpperCamelCase (self       :  Union[str, Any]    ,		a       :  int    ,		a       :  int    ,		a       :  Optional[Any]    ,		a       :  int    )       ->   str:






    """simple docstring"""


    A__       =     TFTransfoXLModel(a    )

    A__  ,			A__       =     model(a    ).to_tuple()

    A__       =     {'input_ids': input_ids_a, 'mems': mems_a}

    A__  ,			A__       =     model(a    ).to_tuple()

    self.parent.assertEqual(hidden_states_a.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )
    self.parent.assertEqual(hidden_states_a.shape    ,		(self.batch_size, self.seq_length, self.hidden_size)    )
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  str    ,		a       :  Optional[int]    ,		a       :  List[Any]    ,		a       :  Optional[Any]    )       ->   Dict:






    """simple docstring"""


    A__       =     TFTransfoXLLMHeadModel(a    )

    A__  ,			A__       =     model(a    ).to_tuple()

    A__       =     {'input_ids': input_ids_a, 'labels': lm_labels}
    A__  ,			A__       =     model(a    ).to_tuple()

    A__  ,			A__       =     model([input_ids_a, mems_a]    ).to_tuple()

    A__       =     {'input_ids': input_ids_a, 'mems': mems_a, 'labels': lm_labels}

    A__  ,			A__       =     model(a    ).to_tuple()

    self.parent.assertEqual(lm_logits_a.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)    )
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)

    self.parent.assertEqual(lm_logits_a.shape    ,		(self.batch_size, self.seq_length, self.vocab_size)    )
    self.parent.assertListEqual(
        [mem.shape for mem in mems_a]    ,		[(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers    ,		)






 def 						_UpperCamelCase (self       :  Tuple    ,		a       :  Any    ,		a       :  int    ,		a       :  Tuple    ,		a       :  Optional[Any]    )       ->   str:






    """simple docstring"""


    A__       =     TFTransfoXLForSequenceClassification(a    )
    A__       =     model(a    )
    self.parent.assertEqual(result.logits.shape    ,		(self.batch_size, self.num_labels)    )






 def 						_UpperCamelCase (self       :  str    )       ->   str:






    """simple docstring"""


    A__       =     self.prepare_config_and_inputs()
    ((A__)  ,			(A__)  ,			(A__)  ,			(A__))       =     config_and_inputs
    A__       =     {'input_ids': input_ids_a}
    return config, inputs_dict




@require_tf
class 	_snake_case(			UpperCAmelCase       , UpperCAmelCase       , unittest.TestCase      ):
 __snake_case:							Tuple						=					(
     (TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
 )
 __snake_case:							Dict						=					() if is_tf_available() else ()
 __snake_case:							Any						=					(
     {
         '''feature-extraction''': TFTransfoXLModel,
         '''text-classification''': TFTransfoXLForSequenceClassification,
         '''text-generation''': TFTransfoXLLMHeadModel,
         '''zero-shot''': TFTransfoXLForSequenceClassification,
     }
     if is_tf_available()
     else {}
 )
 # TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
 __snake_case:							str						=					False
 __snake_case:							Dict						=					False
 __snake_case:							Tuple						=					False
 __snake_case:							int						=					False






 def 						_UpperCamelCase (self       :  Optional[int]    ,		a       :  Tuple    ,		a       :  str    ,		a       :  Any    ,		a       :  Optional[Any]    ,		a       :  List[Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    if pipeline_test_casse_name == "TextGenerationPipelineTests":
       # Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
       # `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
       # tokenizer.
       return True

    return False






 def 						_UpperCamelCase (self       :  Dict    )       ->   Optional[Any]:






    """simple docstring"""


    A__       =     TFTransfoXLModelTester(self    )
    A__       =     ConfigTester(self    ,		config_class=a    ,		d_embed=37    )






 def 						_UpperCamelCase (self       :  int    )       ->   Any:






    """simple docstring"""


    self.config_tester.run_common_tests()






 def 						_UpperCamelCase (self       :  Tuple    )       ->   Tuple:






    """simple docstring"""


    self.model_tester.set_seed()
    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_model(*a    )






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    self.model_tester.set_seed()
    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_lm_head(*a    )






 def 						_UpperCamelCase (self       :  Optional[Any]    )       ->   Any:






    """simple docstring"""


    A__       =     self.model_tester.prepare_config_and_inputs()
    self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*a    )






 def 						_UpperCamelCase (self       :  List[str]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__  ,			A__       =     self.model_tester.prepare_config_and_inputs_for_common()
    A__       =     [TFTransfoXLForSequenceClassification]

    for model_class in self.all_model_classes:
       A__       =     model_class(a    )
       assert isinstance(model.get_input_embeddings()    ,		tf.keras.layers.Layer    )
       if model_class in list_other_models_with_output_ebd:
          A__       =     model.get_output_embeddings()
          assert isinstance(a    ,		tf.keras.layers.Layer    )
          A__       =     model.get_bias()
          assert name is None
       else:
          A__       =     model.get_output_embeddings()
          assert x is None
          A__       =     model.get_bias()
          assert name is None






 def 						_UpperCamelCase (self       :  Dict    )       ->   List[Any]:






    """simple docstring"""


    pass






 @slow
 def 						_UpperCamelCase (self       :  List[str]    )       ->   List[Any]:






    """simple docstring"""


    for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
       A__       =     TFTransfoXLModel.from_pretrained(a    )
       self.assertIsNotNone(a    )






 @unittest.skip(reason='This model doesn\'t play well with fit() due to not returning a single loss.'    )
 def 						_UpperCamelCase (self       :  Any    )       ->   Optional[Any]:






    """simple docstring"""


    pass




@require_tf
class 	_snake_case(			unittest.TestCase      ):






 @unittest.skip('Skip test until #12651 is resolved.'    )
 @slow
 def 						_UpperCamelCase (self       :  List[Any]    )       ->   Union[str, Any]:






    """simple docstring"""


    A__       =     TFTransfoXLLMHeadModel.from_pretrained('transfo-xl-wt103'    )
    # fmt: off
    A__       =     tf.convert_to_tensor([[33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0]]    ,		dtype=tf.intaa    )  # noqa: E231
    # fmt: on
    #  In 1991 , the remains of Russian Tsar Nicholas II and his family
    #  ( except for Alexei and Maria ) are discovered .
    #  The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
    #  remainder of the story . 1883 Western Siberia ,
    #  a young Grigori Rasputin is asked by his father and a group of men to perform magic .
    #  Rasputin has a vision and denounces one of the men as a horse thief . Although his
    #  father initially slaps him for making such an accusation , Rasputin watches as the
    #  man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
    #  the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
    #  with people , even a bishop , begging for his blessing . <eod> </s> <eos>

    # fmt: off
    A__       =     [33,12_97,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,22,17_06,17,2_00_98,5,32_15,21,37,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,62_24,8_31,1_60_02,2,8,6_03,7_89_67,2_95_46,23,8_03,20,25,4_16,5,8,2_32,4,2_77,6,18_55,46_01,3,2_95_46,54,8,36_09,5,5_72_11,49,4,1,2_77,18,8,17_55,1_56_91,3,3_41,25,4_16,6_93,4_25_73,71,17,4_01,94,31,1_79_19,2,2_95_46,78_73,18,1,4_35,23,1_10_11,7_55,5,51_67,3,79_83,98,84,2,2_95_46,32_67,8,36_09,4,1,48_65,10_75,2,60_87,71,6,3_46,8,58_54,3,2_95_46,8_24,14_00,18_68,2,19,1_60,2,3_11,8,54_96,2,2_09_20,17,25,1_50_97,3,24,24,0,33,1,18_57,2,1,10_09,4,11_09,1_17_39,47_62,3_58,5,25,2_45,28,11_10,3,13,10_41,4,24,6_03,4_90,2,7_14_77,2_00_98,10_44_47,2,2_09_61,1,26_04,4,1,3_29,3,0]  # noqa: E231
    # fmt: on
    #  In 1991, the remains of Russian Tsar Nicholas II and his family (
    #  except for Alexei and Maria ) are discovered. The voice of young son,
    #  Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
    #  1883 Western Siberia, a young Grigori Rasputin is asked by his father
    #  and a group of men to perform magic. Rasputin has a vision and
    #  denounces one of the men as a horse thief. Although his father initially
    #  slaps him for making such an accusation, Rasputin watches as the man
    #  is chased outside and beaten. Twenty years later, Rasputin sees a vision
    #  of the Virgin Mary, prompting him to become a priest.
    #  Rasputin quickly becomes famous, with people, even a bishop, begging for
    #  his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
    # Nicholas II and his family were discovered. The voice of <unk> young son,
    # Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>

    A__       =     model.generate(a    ,		max_length=2_00    ,		do_sample=a    )
    self.assertListEqual(output_ids[0].numpy().tolist()    ,		a    )








'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


lowerCAmelCase_     						=				{
    '''configuration_roberta_prelayernorm''': [
        '''ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''RobertaPreLayerNormConfig''',
        '''RobertaPreLayerNormOnnxConfig''',
    ],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''RobertaPreLayerNormForCausalLM''',
        '''RobertaPreLayerNormForMaskedLM''',
        '''RobertaPreLayerNormForMultipleChoice''',
        '''RobertaPreLayerNormForQuestionAnswering''',
        '''RobertaPreLayerNormForSequenceClassification''',
        '''RobertaPreLayerNormForTokenClassification''',
        '''RobertaPreLayerNormModel''',
        '''RobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST''',
        '''TFRobertaPreLayerNormForCausalLM''',
        '''TFRobertaPreLayerNormForMaskedLM''',
        '''TFRobertaPreLayerNormForMultipleChoice''',
        '''TFRobertaPreLayerNormForQuestionAnswering''',
        '''TFRobertaPreLayerNormForSequenceClassification''',
        '''TFRobertaPreLayerNormForTokenClassification''',
        '''TFRobertaPreLayerNormMainLayer''',
        '''TFRobertaPreLayerNormModel''',
        '''TFRobertaPreLayerNormPreTrainedModel''',
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCAmelCase_     						=				[
        '''FlaxRobertaPreLayerNormForCausalLM''',
        '''FlaxRobertaPreLayerNormForMaskedLM''',
        '''FlaxRobertaPreLayerNormForMultipleChoice''',
        '''FlaxRobertaPreLayerNormForQuestionAnswering''',
        '''FlaxRobertaPreLayerNormForSequenceClassification''',
        '''FlaxRobertaPreLayerNormForTokenClassification''',
        '''FlaxRobertaPreLayerNormModel''',
        '''FlaxRobertaPreLayerNormPreTrainedModel''',
    ]


if TYPE_CHECKING:
    from .configuration_roberta_prelayernorm import (
        ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
        RobertaPreLayerNormConfig,
        RobertaPreLayerNormOnnxConfig,
    )

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_roberta_prelayernorm import (
            ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            RobertaPreLayerNormForCausalLM,
            RobertaPreLayerNormForMaskedLM,
            RobertaPreLayerNormForMultipleChoice,
            RobertaPreLayerNormForQuestionAnswering,
            RobertaPreLayerNormForSequenceClassification,
            RobertaPreLayerNormForTokenClassification,
            RobertaPreLayerNormModel,
            RobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_roberta_prelayernorm import (
            TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFRobertaPreLayerNormForCausalLM,
            TFRobertaPreLayerNormForMaskedLM,
            TFRobertaPreLayerNormForMultipleChoice,
            TFRobertaPreLayerNormForQuestionAnswering,
            TFRobertaPreLayerNormForSequenceClassification,
            TFRobertaPreLayerNormForTokenClassification,
            TFRobertaPreLayerNormMainLayer,
            TFRobertaPreLayerNormModel,
            TFRobertaPreLayerNormPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_roberta_prelayernorm import (
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormPreTrainedModel,
        )

else:
    import sys

    lowerCAmelCase_     						=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)


